Research On Intelligent Detection Of Radar Signal And Intelligent Recognition Of Radar Behavior Under The Cluster Cooperative Reconnaissance Of UVA

With the development of electronic countermeasure technology,the electromagnetic environment of modern battlefield is becoming more and more complex.Radar reconnaissance is faced with weak signals,complex modulation types and low signal-tonoise ratio.Conventional radar signal detection methods are insufficient.At the same time,with the advancement of the application of phased array technology,the radar has beam agility capabilities based on the characteristics of electronic scanning,and the diversification of radar working modes is realized through time-division multiplexing of radar resources,which greatly increases the difficulty of radar behavior recognition.In addition,modern warfare presents systematic confrontation,and it is difficult for a single combat platform to meet actual needs.Therefore,this thesis will use the maneuverable and flexible unmanned aerial vehicle to carry the reconnaissance receiver to form a cluster cooperative reconnaissance system,and then combine with the widely used neural network to carry out research on the intelligent detection of radar signals and the intelligent recognition of radar behavior.The main work of this thesis is as follows:1.The shortcomings of the existing radar signal detection methods are analyzed,and a supervised learning radar signal intelligent detection algorithm is proposed by using the radar ambiguity function and the Res Net neural network structure.The algorithm can be used for radar signal detection of different modulation types,and has a good detection effect under the condition of low signal-to-noise ratio.At the same time,this thesis also optimizes the algorithm performance through cluster cooperative reconnaissance.2.This thesis analyzes the insufficiency of conventional radar signal intelligent detection algorithms to deal with unknown modulation type radar signals.Based on the binary hypothesis receiving model,the feature information of noise data in highdimensional space is extracted by using convolutional neural network.Combined with the idea of clustering,an intelligent detection algorithm suitable for radar signals of unknown modulation type is proposed,and the performance of the algorithm is verified by experiments under the cluster cooperative reconnaissance.3.The beam agility characteristic of phased array radar is analyzed,and an intelligent recognition method of radar behavior under cluster cooperative reconnaissance is proposed.The algorithm collects radar signals through cluster cooperation,combines data fusion,integrates the spatial distribution information of radar signals into its identification features,and then performs radar behavior recognition based on the lightweight deep neural network Mobile Net V3,which is more suitable for battlefield environments.And the performance of the algorithm is verified by experiments.4.Aiming at the problem that it is difficult to obtain enough radar behavior sample data for neural network training in the actual battlefield environment,this thesis uses data augmentation and transfer learning methods to test the effectiveness of the proposed radar behavior intelligent recognition algorithm in the case of small samples.And the effect of the method is analyzed by simulation experiments.